Shopify Inventory Forecasting: The Complete Guide for Growing Brands

By the QuikStock Team · Updated February 2026

If you've ever lost a sale because you ran out of your bestseller, or found yourself staring at pallets of unsold inventory eating into your margins, you already know why forecasting matters. The hard part isn't understanding why. It's figuring out how.

Here's the reality: Shopify doesn't do forecasting for you. The native admin tools track what you have. They don't predict what you'll need.

And with Stocky sunsetting in August 2026, even merchants who relied on its basic forecasting features are now looking for alternatives. Shopify's built-in replacement? It handles purchase orders and transfers, but forecasting logic is completely absent.

That leaves you with a choice: stay reactive and keep guessing, or learn to forecast properly.

This guide breaks down inventory forecasting for Shopify merchants who are past the "sell a few items and eyeball it" phase but aren't ready (or willing) to pay enterprise prices. We'll cover the methods that actually work, the metrics you need to track, and how to put it all together without drowning in spreadsheets.

What Is Inventory Forecasting (And Why Shopify Doesn't Do It)

Inventory forecasting predicts how much stock you'll need over a future period. It's the difference between "I think we'll need more t-shirts" and "Based on last year's data and our planned promotion, we need 340 units of the black medium arriving by March 15."

That precision matters because inventory mistakes compound:

Stockouts cost you the immediate sale. They also tank your conversion rate, train customers to shop elsewhere, and waste ad spend driving traffic to out-of-stock products. If you're running Google Shopping or Meta ads, every click to a sold-out product page is money burned.

The scale of the problem: Retailers lose $1.2 trillion globally to stockouts every year. And 69% of shoppers who encounter an out-of-stock product immediately buy from a competitor instead.

Overstock ties up cash you could use elsewhere. It means storage costs, potential markdowns, and worst case: dead inventory you end up liquidating at a loss. For growing brands, that trapped capital can be the difference between funding your next product launch or not.

Shopify's built-in tools help you track inventory. You can see what's in stock, set low-stock alerts, and move inventory between locations. What they don't do is answer: "How much should I order, and when?"

That's forecasting. And for that, you need either a spreadsheet system, a dedicated app, or enough pattern recognition to do it in your head (not recommended past about 20 SKUs).

The Four Forecasting Methods That Matter

There's no single "right" way to forecast. Different methods work for different situations. Here's a quick reference:

1. Time-Series Forecasting (Your Baseline)

This is the bread and butter: looking at past sales to predict future sales. If you sold 100 units last month and 110 the month before, you can reasonably expect something similar next month, adjusted for any known factors.

When it works: Products with consistent demand and enough sales history (at least 3-6 months of data).

The basic formula:

Forecast = Average Daily Sales × Days in Forecast Period

For example, if you've sold 450 units of a product over the last 90 days:

• Average daily sales = 450 ÷ 90 = 5 units/day
• 30-day forecast = 5 × 30 = 150 units

The trap: This assumes the future looks like the past. If you're planning a promotion, launching new marketing, or entering a seasonal period, a simple average will mislead you.

Making it better: Weight recent data more heavily. Your sales from last month matter more than sales from six months ago. A "weighted moving average" gives recent periods more influence:

Weighted Forecast = (Last Month × 3) + (Month Before × 2) + (Two Months Ago × 1) ÷ 6

This responds faster to trends without overreacting to single-week anomalies.

2. Seasonal Forecasting

Most products have seasonal patterns. Swimwear spikes in summer. Gift items surge in Q4. Even products you wouldn't expect have cycles tied to weather, holidays, or cultural moments.

When it works: Products with at least one full year of sales data showing clear seasonal patterns.

How to apply it:

1. Calculate your "seasonal index" for each period (week or month)
2. Compare each period's sales to the overall average
3. Use that index to adjust your baseline forecast

Here's a simplified example:

Your average monthly sales are 100 units. But December historically does 180 units while July does 60.

• December seasonal index = 180 ÷ 100 = 1.8
• July seasonal index = 60 ÷ 100 = 0.6

If your baseline forecast for next December is 120 units (based on recent trends), apply the seasonal index:

Seasonal Forecast = Baseline Forecast × Seasonal Index
Seasonal Forecast = 120 × 1.8 = 216 units

The trap: Over-indexing on a single year's data. If last December was unusually strong because of a viral TikTok moment, using that as your seasonal index will lead to massive overstocking.

The fix: Use 2-3 years of data when possible. If you only have one year, be conservative with your seasonal adjustments.

3. Promotional Forecasting

Running a 20% off sale? Expect a sales spike. But how big?

Promotional forecasting estimates the "lift" from marketing activities so you don't run out mid-campaign (embarrassing and expensive) or over-order and eat into the margins the promotion was supposed to improve.

When it works: You have data from previous similar promotions.

How to calculate lift:

Promotional Lift = (Sales During Promo ÷ Expected Sales Without Promo) - 1

If you normally sell 50 units/week, and a 25% off sale last time drove 85 units:

• Promotional lift = (85 ÷ 50) - 1 = 0.7 or 70% lift

For your next similar promotion, forecast:

Promotional Forecast = Baseline Forecast × (1 + Lift)

Variables that affect lift:

• Discount depth (30% off > 20% off > 10% off)
• Promotion duration
• Marketing spend behind it
• Whether it's a site-wide or product-specific sale
• Customer fatigue (your fifth "biggest sale ever" hits different)

Start conservative. If you've never run a particular type of promotion, assume 30-50% lift and see what actually happens. You can adjust for next time.

4. Qualitative Forecasting (When Data Isn't Enough)

Sometimes you're launching a new product, entering a new market, or facing conditions with no historical precedent. Quantitative methods fail when there's no relevant data to analyze.

When it works: New product launches, first-time events, or major market changes.

How to do it:

• Analogous forecasting: Find a similar product and use its early sales pattern. Launching a new candle scent? Look at how your other candle launches performed.
• Expert judgment: If you've been in your market for years, your intuition has value. Just quantify it: "I think we'll sell 50-80 units in the first month" is more useful than "probably a decent amount."
• Market research: Pre-orders, waitlist signups, and social engagement give signals before you have sales data.

The trap: Optimism bias. Founders consistently overestimate demand for new products. If your gut says 200 units, plan for 120.

Key insight: You don't need perfect forecasts. Businesses using proper inventory management reduce stockouts by 30% compared to those relying on manual tracking.

The Core Metrics for Demand Planning

You can't forecast well without understanding these numbers:

Sales Velocity

This is your average units sold per day, but excluding days when you were out of stock. That distinction matters.

If you sold 60 units over 30 days, but were out of stock for 10 of those days, your true sales velocity is:

Sales Velocity = 60 units ÷ 20 days = 3 units/day

Not 2 units/day. Using average sales (60 ÷ 30) understates actual demand and leads to chronic understocking.

Most Shopify merchants make this mistake. They look at monthly sales, divide by 30, and wonder why they keep running out.

Lead Time

The gap between placing an order with your supplier and having it ready to sell. This includes:

• Supplier processing time
• Manufacturing/production time
• Shipping/transit time
• Receiving and quality check time

For a domestic supplier, this might be 5-10 days. For overseas manufacturing, it could be 60-90 days.

Know your lead times cold. They determine when you need to reorder, and underestimating them is the #1 cause of stockouts for growing brands.

Safety Stock

Your buffer against the unexpected. Demand spikes, shipping delays, supplier problems. Safety stock keeps you selling when things don't go according to plan.

The standard formula:

Safety Stock = (Max Daily Sales × Max Lead Time) - (Average Daily Sales × Average Lead Time)

The standard safety stock formula accounts for variability in both demand and lead time, giving you a buffer against the unexpected.

Reorder Point

The inventory level at which you need to place your next order. If you wait until you hit zero, you'll have stockouts for the entire lead time.

Reorder Point = (Daily Sales Velocity × Lead Time) + Safety Stock

If you sell 5 units/day, lead time is 14 days, and safety stock is 20 units:

Reorder Point = (5 × 14) + 20 = 90 units

When inventory drops to 90 units, place your order. For a deeper dive with Shopify-specific examples, check out how to calculate your reorder point.

Building Your Forecasting System: Three Approaches

Option 1: Spreadsheets (The Starting Point)

If you have fewer than 50 active SKUs and relatively stable demand, a well-built spreadsheet can work.

What you need:

• Sales data export from Shopify (at least 3 months, ideally 12+)
• Lead time data for each supplier
• Formulas for sales velocity, reorder point, and basic forecasting

Pros: Free, full control, you understand exactly what's happening.

Cons: Manual updates, no real-time data, breaks down past ~100 SKUs, doesn't handle variants well, no alerts.

The honest take: Spreadsheets work until they don't. If you're updating a forecast spreadsheet weekly and it's eating hours of your time, you've outgrown it. Most brands hit that point somewhere between $500K and $2M in annual revenue.

Option 2: Dedicated Forecasting Apps

Purpose-built tools like QuikStock, Inventory Planner, Prediko, and Fabrikatör connect directly to Shopify and automate most of the forecasting process.

What they do:

• Pull sales data automatically
• Calculate sales velocity, accounting for stockouts
• Generate forecasts using multiple methods
• Set reorder points and alerts
• Help create purchase orders

Pros: Saves time, handles complexity, adapts to your data, provides alerts before problems happen.

Cons: Monthly cost ($50-300+/month depending on features and order volume).

When to upgrade: If you're spending more than 2-3 hours per week on inventory spreadsheets, or if stockouts/overstocks are costing you more than the software would, it's time.

Option 3: ERP/Full Suite Solutions

NetSuite, Cin7, TradeGecko (now QuickBooks Commerce). These handle inventory as part of broader operations management.

When they make sense: Multi-million dollar operations with complex supply chains, multiple warehouses, and needs beyond forecasting (accounting integration, B2B workflows, manufacturing).

The honest take: Most Shopify brands under $5M/year don't need this level of complexity. You'll pay for features you don't use and spend weeks on implementation.

Common Forecasting Mistakes (And How to Avoid Them)

Mistake 1: Using Average Sales Instead of Sales Velocity

We covered this above, but it's worth repeating because it's so common. If you were out of stock for any period, your average sales understate demand. Use sales velocity (sales per in-stock day) instead.

Mistake 2: Ignoring Lead Time Variability

Your supplier says "2-3 weeks." Sometimes it's 2 weeks. Sometimes it's 5. If you plan for 2 weeks and it takes 5, you're out of stock for 3 weeks.

Fix: Track actual lead times for every order. Use the longer end of the range for planning, not the optimistic number from your supplier's quote.

Mistake 3: Forecasting at the Product Level When Variants Differ

"T-shirts" isn't a useful forecasting unit if your black mediums sell 3x faster than your white XXLs. Forecast at the variant (SKU) level, or you'll have stockouts in popular variants while sitting on slow movers.

Mistake 4: Set-and-Forget Forecasts

Markets change. Demand shifts. A forecast you built in January shouldn't drive March decisions without review.

Fix: Review forecasts monthly at minimum. Compare predicted vs. actual. If you're consistently off by more than 20%, something in your model needs adjustment.

Mistake 5: Not Factoring in What Drove Past Performance

If last November's sales spike was driven by a $50K Meta ads push you're not repeating this year, that spike shouldn't be in your baseline forecast.

Fix: Document what happened during unusual periods. When reviewing historical data, ask "why was this month different?" before assuming the pattern repeats.

Putting It All Together: A Practical Workflow

Here's a repeatable process for Shopify inventory forecasting:

Weekly (15-30 minutes):

1. Review items approaching reorder points
2. Check for any unexpected demand changes
3. Place necessary purchase orders

Monthly (1-2 hours):

1. Compare actual sales vs. forecast for the past month
2. Identify any SKUs where you were off by 20%+
3. Update forecasts for the next 3 months
4. Review lead time performance from recent orders

Quarterly (2-3 hours):

1. Deep analysis of seasonal patterns
2. Review overall inventory health (turnover, carrying costs)
3. Update safety stock levels based on actual variability
4. Plan for upcoming seasonal periods and promotions

Annually:

1. Full year-over-year analysis
2. Evaluate forecasting accuracy and adjust methods
3. Review supplier performance and lead times
4. Set inventory budget and turnover targets

The Real Cost of Getting It Wrong

Let's put numbers to this:

Scenario: Chronic stockouts

Research shows 40% of all lost sales are caused by stockouts. Here's what that looks like for a typical store:

• 100 orders/day average
• 15% of visitors to an OOS product leave and don't return
• Average order value: $75
• Out of stock on 20% of product pages for an average of 10 days/month

Lost revenue: 100 × 0.2 × 10 × 0.15 × $75 = $2,250/month

That's $27,000/year from a "minor" stockout problem.

It gets worse: 51% of ecommerce products experience at least one stockout per year, with average downtime lasting 35 days.

Scenario: Chronic overstock

• $200K in inventory
• 25% is overstock you'll eventually discount by 30%
• Carrying cost: 20% annually (industry average is 20-30%)

Dead capital: $50K tied up Annual carrying cost: $50K × 20% = $10K Markdown losses: $50K × 30% = $15K

Total annual cost: $25K

The impact compounds: lost immediate sales, wasted ad spend, and customers trained to shop elsewhere.

When to Invest in Forecasting Tools

You've outgrown spreadsheets when:

• You have 50+ active SKUs
• You're spending 3+ hours/week on inventory planning
• Stockouts or overstocks happen monthly despite your efforts
• You sell across multiple channels and can't track demand accurately
• Lead times are long (30+ days) and mistakes are costly

At that point, a $100-200/month tool that saves 10 hours of work and prevents $2,000/month in inventory mistakes is one of the highest-ROI investments you can make.

QuikStock was built specifically for Shopify merchants in this situation: past the spreadsheet phase, but not ready to pay $500+/month for enterprise tools with features you'll never use. If that sounds like you, check out how QuikStock handles forecasting.

The Bottom Line

Inventory forecasting isn't about perfect predictions. It's about making better decisions more consistently. Even improving your forecast accuracy from "wild guess" to "reasonable estimate" can save your business thousands in avoided stockouts and reduced overstock.

Start with the basics: track sales velocity (not average sales), know your real lead times, and set reorder points with safety stock built in. That alone will put you ahead of most Shopify merchants.

As you grow, add complexity gradually. Seasonal adjustments, promotional planning, variant-level tracking. Each layer improves your accuracy, but only if the foundation is solid.

The merchants who master this don't just avoid problems. They free up cash for growth, never miss a sale to stockouts, and make inventory decisions in minutes instead of hours. That's the real payoff.